Understory changes in mixed elm stands in response to canopy dieback in Latvia

Abstract

In the eastern Baltic region, warming is favouring the advance of thermophilic tree species and the formation of mixed stands with a higher share of broadleaves, thus contributing to productivity and other ecosystem services of forests. However, the accelerating environmental changes often trigger the spread and intensification of biotic disturbances such as disease outbreaks at unprecedented rates, which might alter the course of stand development. Within the region, elms (Ulmus sp.), which are of low importance as a source of timber, yet highly important in terms of other ecosystem services, are suffering varying degree of dieback due to the Dutch elm disease, which can threaten the existence of species. Still, there are explicit local differences in the susceptibility of trees, which add uncertainties regarding the sustainability of elms. A permanent sampling plot network was established to monitor changes in mixed elms stands in response to the dieback caused by the disease; canopy and understory trees were accounted. Two surveys in 2016 and 2021 have been made. Canopy elms have suffered moderate dieback during the first wave already before the surveys, as ca. 17% of canopy elms were already dead. However, the susceptibility differed among the species (Ulmus glabra exceeded U. laevis), likely due to the preference of the vectors of the disease. In response to canopy openings, self-regeneration occurred, though the balance between the advance regeneration and undergrowth species was affected by the intensity of canopy dieback. The advanced growth of the former canopy species including elms was sufficient under moderate changes in the canopy, while low and high changes favoured undergrowth, suggesting the formation of shrubland. Accordingly, the additional mortality of elms due to Dutch elm disease tended to diverge the development course of the mixed broadleaved stands; hence further monitoring is advised. Keywords: Ulmus sp.; Ophiostoma sp.; advance growth; undergrowth; Dutch elm disease; succession

High-Accuracy Photovoltaic Power Prediction under Varying Meteorological Conditions: Enhanced and Improved Beluga Whale Optimization Extreme Learning Machine

“…A predictive model for financial market system risk based on ELM was constructed and the stability of the model was verified through examples. In terms of healthcare, ELM can help predict the development of diseases and the recovery process of patients [28]. ELM has also achieved results in the field of traffic flow prediction.…”